Fig.REF a shows the performance of models of up to six cycles (18 layers, \(k=3\) ) on CIFAR-10.
CycleNet performs better than the CNN baseline, and the non-cubic CycleNet approaches the performance of a ResNet-18 [1]} with a similar number of parameters.
These results suggest that long-range integration may be instrumental for image classification, but their significance is limited by the small resolution of CIFAR-10 (\(32\times 32\) ).
To confirm our results on higher resolution, we next tested CycleNet on ImageNet (\(128\times 128\) ).
